The technology disclosed herein generally relates to methods and apparatus for detecting and classifying repetitive signals.
A receiver system is any system configured to receive energy waves and process these energy waves to identify desired information carried in the energy waves. As used herein, an “energy wave” is a disturbance that propagates through at least one medium while carrying energy. For examples, energy waves may comprise electromagnetic waves, radio waves, microwaves, sound waves or ultrasound waves.
Typically, a receiver system includes a transducer and a receiver. A transducer may be any device configured to convert one type of energy into another type of energy. The transducers used in a receiver system are typically configured to receive energy waves and convert these energy waves into an electrical signal. An antenna is one example of a transducer. A receiver processes the electrical signal generated by a transducer to obtain desired information from the electrical signal. The desired information includes information about signals carried in the energy waves.
Oftentimes, energy waves are used to carry repetitive signals. A repetitive signal is a signal that has a time period over which some aspect of the signal repeats. Repetitive signals are used in timing operations, synchronization operations, radar operations, sonar operations, and other suitable operations. For example, the characteristics of a repetitive signal may be used to synchronize two or more devices.
In some situations, a receiver system may receive energy waves carrying a repetitive signal but may be unable to identify desired information about the repetitive signal. For example, the receiver system may be unable to detect and/or classify the repetitive signal. Classifying a repetitive signal may include identifying one or more of the following exemplary parameters: frequency, pulse width, type of modulation, period, phase, and/or other suitable characteristics of the repetitive signal.
In at least some known signal processing systems, a plurality of mixed signals (e.g., radar signals) are received by a sensor communicatively coupled to a plurality of blind source separation (BSS) filters. Using signal processing techniques, the BSS filters are configured to separate and identify repetitive signals of interest from the plurality of mixed signals. To improve performance, at least some known BSS filters use pipellning and paralleling techniques.
Blind source separation typically requires measuring signal power out of filters centered at a given set of frequencies. The signals with the highest power can be selected by a BSS algorithm, subject to the hardware limitations of the number of filtered signals available. However, this approach cannot separate signals whose power density is near or below the background noise level (i.e., signals whose signal-to-noise ratio (SNR) is low), since those signals cannot be distinguished from noise on a sample-by-sample basis.
Existing solutions that use blind source separation of very low perceived power signals (hereinafter “low-SNR signals”) typically involve either non-real-time analysis using recorded signals and human intervention or these signals are simply not separated from the data stream being received.
It would be desirable to provide systems and methods that enhance signal detection and separation of repetitive signals in a blind source separation system.